Events for February 22, 2018

The Women in Engineering Advisory Board bring together local middle school girls for a day of exploring STEM education, connecting with current USC students, and hearing from speakers working in the field of engineering.

For questions, please reach out to the KIUEL Programming Committee at vkiuel@gmail.com. We look forward to seeing you at our E-Week events next week!

Abstract: Hippocampal memory prosthesis is a closed-loop system developed to bypass damaged hippocampal regions to restore or enhance memory functions. Different from deep brain stimulation, which delivers stereotypical stimulation patterns to target regions to modulate neural activities, hippocampal memory prostheses utilize biomimetic models and neural code-based stimulation patterns to reinstate neural signal transmission / processing and thus mimic brain functions. In this talk, I will first describe the multi-input, multi-output (MIMO) nonlinear dynamical model being used as the computational basis of hippocampal memory prostheses and the proof-of-principle studies in animal models. Furthermore, I will talk about my more recent results in (1) enhancing memory functions in epilepsy patients with a hippocampal memory prosthesis, (2) developing next-generation computational models and neural interface technologies to fill the gap between the proof-of-principle and clinical hippocampal memory prostheses.

We often capture images of a target scene through glass. For example, we take photographs of the products displayed in the show window, or take photographs of buildings with glass curtain walls. The captured glass image includes the target scene behind the glass as well as undesired reflected scene in front of the glass, since light passes through and is reflected on a pane of glass simultaneously. Such reflection artifacts may degrade the performance of image processing and computer vision techniques when applied to glass images. In this seminar, we first talk about an automatic reflection removal algorithm for multiple glass images taken at slightly different camera locations. Also, with the advent of high-performance LiDAR scanners, large-scale 3D point clouds (LS3DPCs) for real-world scenes are being used in challenging applications. However, LS3DPCs captured by terrestrial LiDAR scanners also suffer from the reflection artifacts since many outdoor real-world structures include glasses. As a next topic, we
define a problem of reflection in LS3DPCs and introduce our current research work on reflection removal for LS3DPCs.

This talk will shed light upon some of these mysteries. I will employ diverse ideas ---from thermodynamics and optimal transportation to partial differential equations, control theory and Bayesian inference--- and paint a picture of the training process of deep networks. Along the way, I will develop state-of-the-art algorithms for non-convex optimization.

The goal of machine perception is not just to classify objects in images but instead, enable intelligent agents that can seamlessly interact with our physical world. I will conclude with a vision of how advances in machine learning and robotics may come together to help build such an Embodied Intelligence.

This lecture satisfies requirements for CSCI 591: Research Colloquium. Please note, due to limited capacity in OHE 100D, seats will be first come first serve.

Biography: Pratik Chaudhari is a PhD candidate in Computer Science at UCLA where he works with Stefano Soatto. His research interests include deep learning, robotics and computer vision. He has worked on perception and control algorithms for safe autonomous urban navigation as a part of nuTonomy Inc. Pratik holds Master's and Engineer's degrees from MIT and a Bachelor's degree from IIT Bombay in Aeronautics and Astronautics.